Collective Intention Recognition and Elder Care

2004

Abstract Based on the assumption that a rational agent will adopt a plan that maximizes the expected utility, we present a utility-based approach to plan recognition problem in this paper. The approach explicitly takes the observed agent's preferences into consideration, and computes the estimated expected utilities of plans to disambiguate competing hypotheses. Online plan recognition is realized by incrementally using plan knowledge and observations to change state probabilities.

AI Communication Journal

Intention recognition is the process of becoming aware of the intentions of other agents, inferring them through observed actions or effects on the environment. Intention recognition enables pro-activeness, in cooperating or promoting cooperation, and in preempting danger. Technically, intention recognition can be performed incrementally as you go along, which amounts to learning. Intention recognition can also use past experience from a database of past interactions, not necessarily with the same agent. Bayesian Networks (BN) can be employed to dynamically sum- marize general statistical evidence, furnishing heuristic information to link with the situation specific infor- mation, about which logical reasoning can take place, and decisions made on actions to be performed, possi- bly involving actions to obtain new observations. This situated reasoning feeds into the BN to tune it, and back again into the logic component. In this article, we provide a review bearing on the state-of-the-art work on intention and plan recognition, which includes a comparison with our recent research, where we address a number of important issues of intention recognition. We also argue for an integrative approach tointention-based decision-making that uses a combination of Logic Programming and Bayesian Networks.

Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, 2013

In this chapter we present an intention-based decision making system. We exhibit a coherent combination of two Logic Programming based implemented systems, Evolution Prospection and Intention Recognition. The Evolution Prospection system has proven to be a powerful system for decision making, designing and implementing several kinds of preferences and useful environment-triggering constructs. It is here enhanced with an ability to recognize intentions of other agents—an important aspect not well explored so far. The usage and usefulness of the combined system are illustrated with several extended examples in different application domains, including Moral Reasoning, Ambient Intelligence, Elder Care, and Game Theory.

Computational Intelligence for Engineering …, 2011

We describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable the recognizing agent to come up with the most likely intentions of the intending agent, i.e. solve one main issue of intention recognition. And, in case of having to make a quick decision, focus on the most important ones. Furthermore, the combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, in order to confirm or disconfirm likely intentions. The absence of this articulation is a main drawback of the approaches using Bayes Networks solely, due to the combinatorial problem they encounter. We explore and exemplify its application, in the Elder Care context, of the ability to perform Intention Recognition and of wielding Evolution Prospection methods to help the Elder achieve its intentions. This is achieved by means of an articulate use of a Causal Bayes Network to heuristically gauge probable general intention #x2013; combined with specific generation of plans involving preferences – for checking which such intentions are plausibly being carried out in the specific situation at hand, and suggesting actions to the Elder. The overall approach is formulated within one coherent and general logic programming framework and implemented system. The paper recaps required background and illustrates the approach via an extended application example.

2002

We are building an agent-oriented system to aid elderly people to live longer in their homes, increasing the duration,of their independence,from round-the-clock care while maintaining,important social connectedness and reducing,caregiver,burden. Independent LifeStyle Assistant TM (I.L.S.A.) is a multiagent system that in- corporates a unified sensing model, probabilistically derived situation awareness, hierarchical task network response planning, real-time action selection control, complex

2013

Intention recognition is one of the core components of mindreading, an important process in social cognition. Human beings, from age of 18 months, have been shown to be able to extrapolate intentions from observed actions, even when the performer failed at achieving the goal. Existing accounts of intention recognition emphasize the use of an intent (plan) library, which is matched against observed actions for recognition. These therefore cannot account for recognition of failed sequences of actions, nor novel actions. In this paper, we begin to tackle these open questions by examining computational models for components of human intention recognition, which emphasize the ability of humans to detect and identify intentions in a sequence of observed actions, based solely on the rationality of movement (its efficiency). We provide a high-level overview of intention recognition as a whole, and then elaborate on two components of the model, which we believe to be at its core, namely, those of intention detection and intention prediction. By intention detection we mean the ability to discern whether a sequence of actions has any underlying intention at all, or whether it was performed in an arbitrary manner with no goal in mind. By intention prediction we mean the ability to extend an incomplete sequence of actions to its most likely intended goal. We evaluate the model, and these two components, in context of existing literature, and in a number of experiments with more than 140 human subjects. For intention detection, our model

We apply decision theoretic techniques to construct non-player characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modeled by a Markov decision process. Intention recognition is used to infer the subtask that the human is currently performing, allowing the helper to assist the human in performing the correct task. Experiments show that the method can be effective, giving near-human level performance in helping a human in a collaborative game.

New Trends in Applied Artificial Intelligence, 2007

Human-Artifact interaction in real world situations is currently an active area of research due to the importance foreseen of the social capabilities of near future robots and other intelligent artifacts in integrating them into the human society. In this paper a new paradigm for mutual intention in human-artifact interactions based on the embodied computing paradigm called Intention through Interaction is introduced with theoretical analysis of its relation to the embodiment framework. As examples of the practical use of the framework to replace traditional symbolic based intention understanding systems, the authors' preliminary work in a real-world agent architecture (IECA) and a natural drawing environment (NaturalDraw) is briefed.

2009

This paper outlines an approach that we are taking for eldercare applications in the smart home, involving cognitive errors and their compensation. Our approach involves high-level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low-level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high-level requirement.

International Journal of Pervasive Computing and Communications, 2015

Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to mod...